金融

Powerful, hardware-agnostic quantum code development for derivatives, portfolios, risk, and more.

Our Clients

Our clients trust Classiq to enable their quantum initiatives, upskill their workforce, and deploy efficient quantum programs

Case Studies

Rainbow Option Pricing Implementation With Intesa Sanapaolo

Define the payoff, not the circuit
Express multi-asset strikes, correlation structures, and discount factors in Classiq’s Python-like language Qmod. You model the cash-flow logic and probability weights; Classiq handles the quantum circuit implementation.

You pick the algorithm, Classiq optimizes the implementation
The platform maps your description to the right quantum Monte Carlo or Amplitude-Estimation routine, then auto-generates a qubit-efficient circuit: co-optimising depth, qubit count, and error to meet tolerance targets.

Actionable on today’s NISQ hardware
Hardware-aware compilation tailors each circuit to current device noise and connectivity, extracting tighter confidence intervals with fewer samples than classical Monte Carlo, all within the limits of near-term processors.

Future-proof by design
When new QPUs arrive, simply resynthesise. The same high-level model re-compiles for the updated gate set or qubit topology, protecting your analytics stack from hardware churn.

Credit Risk Assessment Framework

Traditional credit risk assessment models often struggle with accurately capturing complex dependencies, tail risks, and high-dimensional exposure scenarios. A quantum computing proof-of-concept (PoC) in this domain explores how quantum algorithms can enhance modeling capabilities, particularly through more efficient simulation and optimization techniques.

For example, quantum amplitude estimation could accelerate Monte Carlo simulations, potentially reducing the number of samples needed to calculate risk measures like Value-at-Risk (VaR) or Expected Shortfall. Quantum optimization algorithms may also help solve problems such as risk-weighted asset allocation or counterparty exposure minimization more effectively.

Such a PoC typically involves benchmarking quantum approaches against classical baselines, evaluating computational efficiency, and identifying where quantum offers a meaningful advantage. While early-stage, the exploration helps financial institutions understand how quantum computing may support more adaptive, data-intensive risk models.

Quantum Finance Applications

Monte Carlo Methods

Quantum amplitude estimation for derivative pricing
Heston model implementation with stochastic volatility
Path-dependent option pricing algorithms
O(1/N) convergence vs classical O(1/✓/N)

Portfolio Optimization

Multi-period portfolio optimization
Quantum algorithms for non-convex problems
Constraint handling through penalty formulation
CVaR and advanced risk measures

Risk Assessment

Credit risk analysis with regime-switching models
Market risk evaluation using quantum algorithms
Stochastic volatility implementation
Enhanced computational efficiency for VaR

Enable Your Quantum Initiatives

Quantum Team Building

If you and your team are getting started with quantum computer programming, Classiq’s hands-on quantum training program is built for technical professionals. You’ll begin with a focused introduction to quantum computing fundamentals: qubits, quantum gates, and circuit models. Next, you’ll explore key quantum algorithms such as QAOA, VQE, and Grover’s, with an emphasis on practical implementation. The core of the training is onboarding to the Classiq platform, where you’ll learn high-level quantum algorithm development, resource-aware quantum circuit design, and hardware-aware optimization. This program equips developers, engineers, and researchers with the skills to build scalable quantum applications from day one.

Quantum Use-Case Implementation

Classiq’s Use-Case Scoping and Implementation Program is designed to guide teams through the full lifecycle of quantum application development. Whether you're exploring quantum for the first time or scaling an R&D initiative, our experts work closely with you to identify high-impact quantum use cases, define algorithmic approaches, and map requirements to current hardware capabilities. From initial use-case selection to algorithm synthesis and execution on quantum processors, the program is tailored to your project’s complexity and your team’s quantum maturity. It's a practical, results-driven pathway to developing and deploying real-world quantum solutions with clarity, speed, and technical confidence.

Advanced Quantum Application Development

Classiq’s Advanced Quantum Application Development offering is designed for teams looking to elevate their quantum work into scalable, future-ready solutions. This offering supports the development of complex quantum circuits using Classiq’s high-level synthesis platform, enabling modular, optimized, and hardware-agnostic quantum algorithm design. It’s ideal for organizations aiming to turn their quantum initiatives into long-term assets, reusable components, or proprietary IP. Whether refining advanced algorithms like QAOA or VQE, or preparing applications for next-gen quantum hardware, this offering helps teams industrialize their quantum development and ensure their work is robust, efficient, and strategically aligned with long-term R&D goals.

Explore Quantum Finance Applications

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